<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html>
  <head>
    <title>TurboParser</title>
    <link type="text/css" rel="stylesheet" href="http://www.cs.cmu.edu/~nasmith/nasstyle.css">
  </head>

   <body>
<h1>TurboParser (Dependency Parser with Linear Programming)</h1>

<div class="mybox">
<table>
<tr><td rowspan=3 valign=top><img src=turbo-parser.png width=180></td>
<td>This page provides a link to <b>TurboParser</b>, a free multilingual dependency parser developed by <a href="http://www.cs.cmu.edu/~afm">Andr&eacute; Martins</a>.<br></td></tr>
<tr><td>It is based on joint work with 
<a href="http://www.cs.cmu.edu/~nasmith">Noah Smith</a>, 
<a href="http://www.lx.it.pt/~mtf">M&aacute;rio Figueiredo</a>, 
<a href="http://www.cs.cmu.edu/~epxing">Eric Xing</a>, 
<a href="http://www.isr.ist.utl.pt/~aguiar">Pedro Aguiar</a>. 
</td></tr>
<tr><td> </td>
</tr>
</table>
</div>

<h3>Background</h3>
<p>
Dependency parsing is a lightweight syntactic formalism that relies on lexical relationships between words. 
<i>Nonprojective</i> dependency grammars may generate languages that are not context-free, offering a formalism 
that is arguably more adequate for some natural languages.
Statistical parsers, learned from treebanks, have achieved the best performance in this task. While only local 
models (arc-factored) allow for exact inference, it has been shown that including non-local features and performing 
approximate inference can greatly increase performance.
</p>

<p>
This package contains a C++ implementation of a 
dependency parser based on the papers [1,2,3,4,5] below. 
The latest version of this package also contains C++ implementations of 
a POS tagger, a semantic role labeler, a entity tagger, 
a coreference resolver, and a constituent (phrase-based) parser. 
The relevant references are the papers [6,7,8,9] below. 
<p>
This package allows:
<ul>
<li>learning a parser/tagger/semantic parser/entity tagger/coreference resolver from a treebank,</li>
<li>running a parser/tagger/semantic parser/entity tagger/coreference resolver on new data,</li>
<li>evaluating the results against a gold-standard.</li>
</ul>
</p>

<br/>

<h3>Demo</h3>
<ul>
<li>
  <a href="http://demo.ark.cs.cmu.edu/parse">ARK Syntactic &amp; Semantic Parsing Demo</a>
</li>
</ul>

<h3>News</h3>
<p>
<b>We released TurboParser v2.3 on November 6th, 2015!</b>
This version introduces some new features:
<ul>
<li>
A named entity recognizer (TurboEntityRecognizer) based on the 
Illinois Entity Tagger (ref. [7] below).
</li>
<li>
A coreference resolver (TurboCoreferenceResolver) based on the
Berkeley Coreference Resolution System (ref. [8] below).
</li>
<li>
A constituent parser based on dependency-to-constituent reduction, 
implementing ref. [9] below.
</li>
<li>
A dependency labeler, TurboDependencyLabeler, that can optionally be applied 
after the dependency parser.
</li>
<li>
Compatibility with MS Windows (using MSVC) and with C++0x.
</li>
</ul>
</p>
<p>
<b>We released TurboParser v2.2 on June 26th, 2014!</b>
This version introduces some new features:
<ul>
<li>
A Python wrapper for the tagger and parser (requires Cython 0.19).
</li>
<li>
A semantic role labeler (TurboSemanticParser) implementing ref. [6] below.
</li>
</ul>
</p>
<p>
<b>We released TurboParser v2.1 on May 23th, 2013!</b>
This version introduces some new features:
<ul>
<li>
The full model has now third-order parts for grand-siblings and tri-siblings (see ref. [5] below).
</li>
<li>
Compatibility with MS Windows (using MSVC).
</li>
</ul>
</p>
<p>
<b>We released TurboParser v2.0 on September 20th, 2012!</b>
This version introduces a number of new features:
<ul>
<li>
The parser does not depend anymore on CPLEX (or any other non-free LP solver).
Instead, the decoder is now based on <a href="http://www.ark.cs.cmu.edu/AD3">AD3</a>, our free library for
approximate MAP inference.
</li>
<li>
The parser now outputs <i>dependency labels</i> along with the backbone structure.
</li>
<li>
As a bonus, we now provide a trainable part-of-speech tagger, called <i>TurboTagger</i>, which can be used in standalone mode, or to provide part-of-speech 
tags as input for the parser. TurboTagger has state-of-the-art accuracy for English (97.3% on section 23 of the Penn Treebank) and is fast (~40,000 tokens per second).
</li>
<li>
The parser is much faster than in previous versions. You may choose among a basic arc-factored parser (~4,300 tokens per second), a 
standard second-order model with consecutive sibling and grandparent features (the default; ~1,200 tokens per second), and 
a full model with head bigram and arbitrary sibling features (~900 tokens per second).
</li>
</ul>
<b>Note:</b> The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM.
</p>

<!--p>
This software has the following external dependencies: <a href="http://www.ark.cs.cmu.edu/AD3">AD3</a>, a library for
approximate MAP inference; <a href="http://eigen.tuxfamily.org/">Eigen</a>, a template
library for linear algebra; <a href="http://code.google.com/p/google-glog/">google-glog</a>, a library for logging; 
<a href="http://code.google.com/p/gflags/">gflags</a>, a library
for commandline flag processing. All these libraries are free software and are 
provided as tarballs in this package.
</p-->

<p>
To run this software, you need a standard C++ compiler. 
This software has the following external dependencies: <a href="http://www.ark.cs.cmu.edu/AD3">AD3</a>, a library for
approximate MAP inference; <a href="http://eigen.tuxfamily.org/">Eigen</a>, a template
library for linear algebra; <a href="http://code.google.com/p/google-glog/">google-glog</a>, a library for logging; 
<a href="http://code.google.com/p/gflags/">gflags</a>, a library
for commandline flag processing. All these libraries are free software and are 
provided as tarballs in this package.
</p>
<p>
This software has been tested in several Linux platforms. It has also
successfully compiled in Mac OS X and MS Windows (using MSVC). 
</p>
<br/>


<h3>Further Reading</h3>
<p>
The main technical ideas behind this software appear in the papers:
<br /><br />
<table>
<tr valign="top"><td>[1]&nbsp;</td>
<td>
Andr&eacute; F. T. Martins, Noah A. Smith, and Eric P. Xing.<br />
<a href="http://www.cs.cmu.edu/~afm/Home_files/acl2009.pdf" title="http://www.cs.cmu.edu/~afm/Home_files/acl2009.pdf">Concise Integer Linear Programming Formulations for Dependency Parsing</a>.<br>
Annual Meeting of the Association for Computational Linguistics (ACL'09), Singapore, August 2009.<br />
</td></tr>
<tr valign="top"><td>[2]&nbsp;</td>
<td>
Andr&eacute; F. T. Martins, Noah A. Smith, and Eric P. Xing.<br />
<a href="http://www.cs.cmu.edu/~afm/Home_files/icml2009.pdf">Polyhedral Outer Approximations with Application to Natural Language Parsing</a>.<br />
International Conference on Machine Learning (ICML'09), Montreal, Canada, June 2009.<br />
</td></tr>
<tr valign="top"><td>[3]&nbsp;</td>
<td>
Andr&eacute; F. T. Martins, Noah A. Smith, Eric P. Xing, M&aacute;rio A. T. Figueiredo, Pedro M. Q. Aguiar.<br />
<a href="http://www.cs.cmu.edu/~afm/Home_files/emnlp2010.pdf">TurboParsers: Dependency Parsing by Approximate Variational Inference</a>.<br />
Empirical Methods in Natural Language Processing (EMNLP'10), Boston, USA, October 2010.<br>
</td></tr>
<tr valign="top"><td>[4]&nbsp;</td>
<td>
Andr&eacute; F. T. Martins, Noah A. Smith, M&aacute;rio A. T. Figueiredo, Pedro M. Q. Aguiar.<br />
<a href="http://www.cs.cmu.edu/~afm/Home_files/emnlp2011b.pdf">Dual Decomposition With Many Overlapping Components</a>.<br />
Empirical Methods in Natural Language Processing (EMNLP'11), Edinburgh, UK, July 2011.<br>
</td></tr>
<tr valign="top"><td>[5]&nbsp;</td>
<td>
Andr&eacute; F. T. Martins, Miguel B. Almeida, Noah A. Smith.<br />
<a href="http://www.cs.cmu.edu/~afm/Home_files/acl2013short.pdf">Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers</a>.<br />
In Annual Meeting of the Association for Computational Linguistics (ACL'13), Sofia, Bulgaria, August 2013.<br>
</td></tr>
<tr valign="top"><td>[6]&nbsp;</td>
<td>
Andr&eacute; F. T. Martins and Mariana S. C. Almeida.<br />
<a href="http://www.cs.cmu.edu/~afm/Home_files/semeval2014_task8.pdf">Priberam: A Turbo Semantic Parser with Second Order Features</a>.<br />
In International Workshop on Semantic Evaluation (SemEval), task 8: Broad-Coverage Semantic Dependency Parsing, Dublin, August 2014.<br>
</td></tr>
<tr valign="top"><td>[7]&nbsp;</td>
<td>
Lev Ratinov and Dan Roth.<br />
<a href="http://cogcomp.cs.illinois.edu/papers/RatinovRo09.pdf">Design Challenges and Misconceptions in Named Entity Recognition</a>.<br />
In International Conference on Natural Language Learning (CoNLL'09), 2009.<br>
</td></tr>
<tr valign="top"><td>[8]&nbsp;</td>
<td>
Greg Durrett and Dan Klein.<br />
<a href="http://www.eecs.berkeley.edu/~gdurrett/papers/durrett-klein-emnlp2013.pdf">Easy Victories and Uphill Battles in Coreference Resolution</a>.<br />
Empirical Methods in Natural Language Processing (EMNLP'13), 2013.<br>
</td></tr>
<tr valign="top"><td>[9]&nbsp;</td>
<td>
Daniel F.-Gonz&aacute;lez and Andr&eacute; F. T. Martins.<br />
<a href="http://www.cs.cmu.edu/~afm/Home_files/acl2015_reduction.pdf">Parsing As Reduction</a>.<br />
In Annual Meeting of the Association for Computational Linguistics (ACL'15), Beijing, China, August 2015.<br>
</td></tr>
</table>
</p>

<br/>

<h3>Download</h3>
<p>
The latest version of TurboParser is <a href="http://www.cs.cmu.edu/~afm/TurboParser/TurboParser-2.3.0.tar.gz">TurboParser v2.3.0 [~5.4MB,.tar.gz format]</a>. 
See the <a href="http://www.cs.cmu.edu/~afm/TurboParser/README">README</a> file for instructions for compilation, running, and file formatting. 
It does <i>not</i> include the data sets used in the papers; 
for information about how to get these data sets, please go to <a href="http://nextens.uvt.nl/~conll">http://nextens.uvt.nl/~conll</a>. 
Bear in mind that some data sets must be separately licensed through the <a href="http://www.ldc.upenn.edu/">LDC</a>.
</p>
<p>
In addition, we provide separately the following pre-trained models (notice that these are very large files):
<ul>
<li>An English tagger trained on the sections 02-21 of the Penn Treebank. 
Click <a href="sample_models/english_proj_tagger.tar.gz">here</a> to download this model [~3.3MB, .tar.gz format].
Then, uncompress this model and save it in a local folder (e.g. as models/english_proj_tagger.model). 
To tag a new file &lt;input-file&gt;, type:<br/>
 <br/>
 <div class="mybox" style="font-family:Courier">
./TurboTagger --test \<br/>
--file_model=models/english_proj_tagger.model \<br/>
--file_test=&lt;input-file&gt; \<br/>
--file_prediction=&lt;output-file&gt; \<br/>
--logtostderr<br/>
</div>
<br/>
Check the <a href="http://www.cs.cmu.edu/~afm/TurboParser/README">README</a> for file formatting instructions and additional options.
<li>First, second, and third-order English parsers trained on the sections 02-21 of the Penn Treebank, 
with dependencies extracted using the head-rules of Yamada and Matsumoto, through <a href="http://w3.msi.vxu.se/~nivre/research/Penn2Malt.html">Penn2Malt</a>.
Click <a href="sample_models/english_proj_parser.tar.gz">here</a> to download these models [~1.8GB, .tar.gz format].
Uncompress this file and save the models in a local folder (e.g. as models/english_proj_parser_model-{basic,standard,full}.model). 
To parse a new file &lt;input-file&gt; in CoNLL format, type:<br/>
 <br/>
 <div class="mybox" style="font-family:Courier">
./TurboParser --test \<br/>
--file_model=models/english_proj_parser_model-standard.model \<br/>
--file_test=&lt;input-file&gt; \<br/>
--file_prediction=&lt;output-file&gt; \<br/>
--logtostderr<br/>
</div>
<br/>
Check the <a href="http://www.cs.cmu.edu/~afm/TurboParser/README">README</a> for file formatting instructions and additional options.
<!--li>Another English parser trained in the dataset provided in the CoNLL 2008 shared task (ignoring the semantic dependencies). 
As described <a href="http://www.yr-bcn.es/conll2008">here</a>, 
this dataset was obtained from the sections 02-21 of the Penn Treebank by 
applying a different set of rules. Unlike the dataset used to train the previous model, 
this one contains non-projective arcs.<br>
Click <a href="sample_models/english.tar.gz">here</a> to download this model [~1.4 GB, .tar.gz format].
<li>A model trained in the Arabic dataset provided in the CoNLL-X shared task. <br>
Click <a href="sample_models/arabic.tar.gz">here</a> to download this model [~225 MB, .tar.gz format].
<-->
<li>First, second, and third-order Arabic parsers trained in the Arabic dataset provided in the CoNLL-X shared task. 
Click <a href="sample_models/arabic_parser.tar.gz">here</a> to download these models [~520 MB, .tar.gz format].
Uncompress this file and save the models in a local folder (e.g. as models/arabic_model-{basic,standard,full}.model). 
To parse a new file &lt;input-file&gt; in CoNLL format, type:<br/>
 <br/>
 <div class="mybox" style="font-family:Courier">
./TurboParser --test \<br/>
--file_model=models/arabic_parser_model-standard.model \<br/>
--file_test=&lt;input-file&gt; \<br/>
--file_prediction=&lt;output-file&gt; \<br/>
--logtostderr<br/>
</div>
<br/>
Check the <a href="http://www.cs.cmu.edu/~afm/TurboParser/README">README</a> for file formatting instructions and additional options.

<li>Taggers and parsers for <a href="nasmith_models/kin-turbo-v1.0.tgz">Kinyarwanda</a> and
<a href="nasmith_models/mlg-turbo-v1.0.tgz">Malagasy</a>.
There is
a <a href="nasmith_models/README">README</a>
specifically for these models.  They require TurboParser v. 2.0.2.
<li>Farsi parser trained on the <a href="http://dadegan.ir/en">Dadegan Persian treebank</a>. Click <a href="sample_models/farsi_parser.tar.gz">here</a> to download the model [~530 MB, .tar.gz format]. This model requires TurboParser v 2.0.2.
Associated Farsi NLP tools can be found <a href="https://github.com/wfeely/farsiNLPTools">here</a>.

<li>Parsers that generate Stanford-style dependencies can be found <a href="http://www.ark.cs.cmu.edu/TBSD/">here</a>.</li>
<li>A parser trained on the English Web Treebank for Stanford basic dependencies can be found <a href="../LexSem/#syntax">here</a>.</li>
</ul>

<p>
Finally, a script "parse.sh" is provided in this package that allows you to tag and parse 
free text (in English, one sentence per line) with the models above. Just type:
<br/>
<div class="mybox" style="font-family:Courier">
./scripts/parse.sh &lt;filename&gt;
</div>
</br>
where <i>&lt;filename&gt;</i> is a text file with one sentence per line. If no filename is 
specified, it parses <i>stdin</i>, so e.g.
<br/>
<div class="mybox" style="font-family:Courier">
echo "I solved the problem with statistics." | ./scripts/parse.sh
</div>
<br/>
yields
<br/>
<div class="mybox" style="font-family:Courier">
1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;I&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;PRP&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;PRP&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;SUB<br/>
2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;solved&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;VBD&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;VBD&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ROOT<br/>
3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;the&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DT&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;DT&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NMOD<br/>
4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;problem&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;OBJ<br/>
5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;with&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;IN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;IN&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;VMOD<br/>
6&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;statistics&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NNS&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;NNS&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;PMOD<br/>
7&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;_&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;P<br/>
<!--table>
<tr>
<td>1</td><td>I</td><td>_</td><td>PRP</td><td>PRP</td><td>_</td><td>2</td><td>SUB</td>
</tr>
<tr>
<td>2</td><td>solved</td><td>_</td><td>VB</td><td>VBD</td><td>_</td><td>0</td><td>ROOT</td>
<tr>
</tr>
<td>3</td><td>the</td><td>_</td><td>DT</td><td>DT</td><td>_</td><td>4</td><td>NMOD</td>
<tr>
</tr>
<td>4</td><td>problem</td><td>_</td><td>NN</td><td>NN</td><td>_</td><td>2</td><td>OBJ</td>
<tr>
</tr>
<td>5</td><td>with</td><td>_</td><td>IN</td><td>IN</td><td>_</td><td>2</td><td>VMOD</td>
<tr>
</tr>
<td>6</td><td>statistics</td><td>_</td><td>NN</td><td>NNS</td><td>_</td><td>5</td><td>PMOD</td>
<tr>
</tr>
<tr>
<td>7</td><td>.</td><td>_</td><td>.</td><td>.</td><td>_</td><td>2</td><td>P</td>
</tr>
</table-->
</div>
</p>

</p>
<p> Older versions: 
<ul>
<li>
<a href="http://www.cs.cmu.edu/~afm/TurboParser/TurboParser-2.2.0.tar.gz">TurboParser v2.2.0 [~2.8MB,.tar.gz format]</a>. 
<li>
<a href="http://www.cs.cmu.edu/~afm/TurboParser/TurboParser-2.1.0.tar.gz">TurboParser v2.1.0 [~2.5MB,.tar.gz format]</a>. 
<li>
<a href="http://www.cs.cmu.edu/~afm/TurboParser/TurboParser-2.0.2.tar.gz">TurboParser v2.0.2 [~2.5MB,.tar.gz format]</a>. 
<li>
<a href="http://www.cs.cmu.edu/~afm/TurboParser/TurboParser-2.0.1.tar.gz">TurboParser v2.0.1 [~2.5MB,.tar.gz format]</a>. 
<li>
<a href="http://www.cs.cmu.edu/~afm/TurboParser/TurboParser-2.0.tar.gz">TurboParser v2.0 [~3.2MB,.tar.gz format]</a>.
<li> 
<a href="http://www.cs.cmu.edu/~afm/TurboParser/turboparser-0.1.tar.gz">TurboParser v0.1 [~2.5Mb,.tar.gz format]</a>. 
Along with this distribution, we released 
an <a href="TurboParser-0.1/sample_models/english_proj.tar.gz">English parser</a> trained on the sections 02-21 of the Penn Treebank, 
with dependencies extracted using the head-rules of Yamada and Matsumoto [~1.2 GB, .tar.gz format];
<a href="TurboParser-0.1/sample_models/english.tar.gz">another English parser</a> trained in the dataset provided in the CoNLL 2008 shared task [~1.4 GB, .tar.gz format];
an <a href="TurboParser-0.1/sample_models/arabic.tar.gz">Arabic parser</a> trained in the CoNLL-X dataset [~225 MB, .tar.gz format];
a <a href="TurboParser-0.1/sample_models/run_pretrained.sh">script</a> to apply these models to parse new data.
</ul>

<br/>
<h3>Contributing to TurboParser</h3>
<p>For questions, bug fixes and comments, please e-mail <i>afm [at] cs.cmu.edu</i>.</p>
<p>To contribute to TurboParser, you can fork the following github repository: <a href="http://github.com/andre-martins/TurboParser">http://github.com/andre-martins/TurboParser</a>.</p>
<p>To receive announcements about updates to TurboParser, <a href="https://mailman.srv.cs.cmu.edu/mailman/listinfo/ark-tools">join the ARK-tools mailing list</a>.</p>
<br/>

<h3>Acknowledgments</h3>
<p>A. M. was supported by a FCT/ICTI grant through
the CMU-Portugal Program, and by Priberam. This
work was partially supported by the FET programme
(EU FP7), under the SIMBAD project (contract 213250), 
by National Science Foundation grant  IIS-1054319,
and by the QNRF grant NPRP 08-485-1-083.</p>

</body>
</html>
